There are a great number of publications that apply different methods to estimate soil moisture from optical satellite imagery. However, none of the proposed methods have considered correcting solar illumination error that is caused by variation in topography before estimating soil moisture. In this research, an integrated approach is developed to improve the estimation of soil moisture. The integration is represented by removing the solar-illumination error from the data. Several modifications were made in the Ts-VI space based on the Universal Triangle Relationship. The data used in the research are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. The research results show that the surface-illumination error, which is caused by variation in topography, misleads the estimation of soil moisture index. Based on statistical and visual analysis, the results are improved with removing error. The method is further enhanced with the application of enhanced vegetation index (EVI) to the Ts-VI relationship.

Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)